|
Competition |
Theme |
Organizer(s) |
Contact |
First Neural Connectomics Challenge: From imaging to connectivity(IJCNN 2014) |
This competition aims at advancing the research on network structure reconstruction algorithms from neurophysiological data, including causal discovery methods. The challenge will make use of realistic simulations of real networks of neurons observed via calcium fluorescence recordings. The participants are expected to come up with efficient algorithms for analyzing time series and large data-sets. |
Isabelle Guyon, Olav Stetter, Demian Battaglia, Javier Orlandi, Jordi Soriano Fradera, Mehreen Saeed, Alexander Statnikov, Bisakha Ray, Alice Guyon, Gavin Cawley, Gideon Dror, Hugo-Jair Escalante, Vincent Lemaire, Sisi Ma, Florin Popescu, and Joshua Vogelstein |
guyon@chalearn.org |
Real-Parameter Numerical Optimization(CEC 2014) |
This competition aims at evaluating the current state of the art in single objective optimization with bound constraints and to propose novel benchmark problems with diverse characteristics. The algorithms will be evaluated with very small number of function evaluations to large number of function evaluations. |
J. J. Liang, P. N. Suganthan, Bo Liu, B. Y. Qu, Qingfu Zhang, and Qin Chen |
EPNSugan@ntu.edu.sg |
Evolutionary Computation for Dynamic Optimization Problems(CEC 2014) |
This competition focuses on the real-parameter (continuous) dynamic function optimization problems along with a dynamic combinatorial optimization problem. The organizers will use the revised version of the benchmark DOPs (Dynamic Optimization Problems) in continuous space used for the 2009 and 2012 Competitions under the IEEE CEC for DOPs and a Dynamic Travelling Salesman Problem in combinatorial space as the benchmark DOPs. |
Changhe Li, Michalis Mavrovouniotis, Shengxiang Yang, and Xin Yao |
changhe.lw@gmail.com |
Optimisation of Problems with Multiple Interdependent Components(CEC 2014) |
The competition provides a platform for comparing computational intelligence approaches to solve multi-component optimization problems. The organizers mainly focus on the combination of the TSP and the Knapsack problems in this context. In particular, Euclidian 2D Travelling Salesperson instances are combined with 0-1-Knapsack instances in such a way that it reflects characteristics of the real-world problems; for example, the total weight of the items in the knapsack influences the travel speed of a traveler. |
Sergey Polyakovskiy, Markus Wagner, Mohammad Reza Bonyadi, Frank Neumann,and Zbignew Michalewicz |
markus.wagner@adelaide.edu.au |